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As one of the main hydrogen production devices, the performance of the proton exchange membrane electrolytic cell (PEMEC) is directly related to the geometric design of the flow channel. In this paper, a new orthogonally sinusoidal channel with periodic fluctuations in both horizontal and vertical planes is proposed. The effects of amplitude and angular frequency in two orthogonally sinusoidal channels with the same or opposite frequency on PEMEC performance are studied by numerical simulation. The results show that the spatial amplitude and angular frequency effectively improve the electrochemical performance and mass transfer performance of PEMEC. Compared with Case 1-1, the polarization performance of Case 7-2 is increased by 16.39% because of the largest spatial amplitude. The oxygen content at 40 mm away from the inlet in the diffusion layer is also decreased by 42.63%. The horizontal angular frequency can increase the hydrogen content by 11.07% at most in Case 5-2. However, the increase in vertical angle frequency will inhibit the oxygen removal on the anode side. The sinusoidal flow channel in orthogonal spaces will provide a particular reference for the design of PEMEC in the future. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of an improved quasisteady flow model (iQSFM) that applies comprehensive parasitic losses to the quasisteady flow model (QSFM) considering an oscillating flow, which is the actual type of flow occurring in a regenerator. Validation of iQSFM was evaluated by comparing it with a QSFM based on the experimental results of a RE-1000 regenerator. Compared to QSFM, iQSFM improved the prediction accuracy by reducing the indicated power error from 66.7% to 24.9% and the efficiency error from 35.3% to 9.4%. In addition, the prediction accuracy of iQSFM was compared when the oscillating flow and the steady flow correlation were applied to a regenerator. When iQSFM applied an oscillating flow correlation to the regenerator, it predicted the experimental results of RE-1000 slightly more accurately than in a steady flow correlation. Finally, the engine performance and parasitic losses were analyzed through a parameter study of RE-1000 using iQSFM. Through this, it was confirmed with iQSFM that the RE-1000 is designed to maximize the engine performance by minimizing the parasitic losses. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

Summary: With increasing global warming, the new energy industry has become the mainstay for achieving a sustainable development of human society. The effective diffusion of leading technologies is the source of social benefits obtained from enterprise core technologies, and it is also a necessary factor for the transformation and optimization of industry. In the international context of energy conservation and emission reduction, systematically and comprehensively studying the diffusion mechanism of leading technologies in new energy industry has important theoretical and practical significance for promoting the diffusion of leading technologies in the new energy industry. Based on the introduction of the cellular automaton model and its extension model, this paper constructs the diffusion model of new energy industry's leading technology and analyzes the diffusion mechanism. This mechanism shows that in the case of the market and policy environments in nowadays, the diffusion of new energy industry's leading technology is mainly influenced by promotion intensity, market share of positive and negative idea leaders, as well as the enterprise scale of strong chain and weak chain. This influence is verified by simulation analysis. Based on the diffusion mechanism, suggestions are presented for promoting the diffusion mechanism of new energy industry leading technology. These suggestions aim to provide a reference for the government, industry associations, and enterprises in making relevant decisions. The novelty of the work is: At the industrial level, this paper explores both the development law and diffusion mechanism of the leading technology in the new energy industry, which is conducive to improving and enriching the theoretical system of technological innovation.This paper uses simulation analysis to verify the diffusion mechanism of leading technologies in the new energy industry. This approach effectively improves the diffusion level of leading technology in the new energy industry and provides a reference for energy upgrading. [ABSTRACT FROM AUTHOR]

Summary: The intermittent, random, and uncertain characteristics of the energy harvested from solar or wind are threatening the commercial efficiency and reliability of power system. With the continuous expansion of renewable energy installation scale, it has become a new challenge to improve the level of renewable energy accommodation and reduce the rate of renewable energy abandonment. Power system production simulation provides basis for the planning and development of renewable energy to assess the capacity of renewable energy accommodation in the power grid accurately. Traditional power system production simulation ignores sequential variation characteristics of load and new energy power output, thus bringing non‐negligible errors. Moreover, most reported studies ignore the impact of power source location on the level of renewable energy accommodation. This paper is therefore prepared with the objective to establish a time‐series production simulation model of multi‐energy power system and carry out multipoint layout planning and design of multi‐energy power supply. To achieve this objective, extensive literature reviews are conducted with more attention given to recent studies on the field. On the basis of the traditional production simulation model, this paper adds the power flow constraints of the transmission network, and proposes a time‐series production simulation model. A multipoint layout planning and design method for multi‐energy power systems is proposed to give full play to the synergistic effects of natural resources, to achieve optimal resource allocation, and to provide a theoretical basis for improving the consumption of wind and renewable energy. The method is simulated with the actual meteorological and operation data of a province power grid in Northwest China. The simulation results of different scenarios are compared and analyzed, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

Summary: The latest breakthroughs in artificial intelligence resulted in their adoption in the electric vehicle's battery management systems. Simultaneously, the remarkable increase in the number of embedded systems in electric vehicles (EVs) has led to the search for new optimized state of charge (SOC) estimation strategies suitable for implementation on small and limited MCUs. In this regard, this paper provides a design and quantization processes of the 1D convolutional neural network (1D CNN) and the GRU‐recurrent neural network (GRU‐RNN), devoted for SOC estimation. Both the NN algorithms are designed and trained using data collected from an 18650 Li‐ion battery. Furthermore, to account for the limited computational resources of the EV MCUs, the pre‐trained models are then converted into highly optimized C‐codes using the latest model quantification techniques. In this regard, this paper investigates, the performances of the STM32Cube.AI and the TensorFlow Lite for microcontroller (TFLite Micro), in quantizing the activation functions and weights of the proposed NN models. This is achieved by converting these latter from 32‐bits floating‐point to 8‐bit integer precision. The obtained experimental results, under different profiles, indicate that the 1D CNN is more accurate than the GRU‐based model with root mean squared error (RMSE) of 2.33% and mean absolute error (MAE) of 1.62%. Furthermore, the impact of the quantization using STM32Cube.AI is compared with that using the TFLite Micro. The obtained results, demonstrated the superiority of the 1D CNN model quantized using the STM32Cube.AI. This latter reduces the flash memory size of the 1D CNN model from 10.82 to 2.89 KB and the RAM size from 2.49 to 1.04 KB, compared to the TFLite Micro, which reduces the RAM size from 7.0107 to 4.23 KB, and the flash from 43.361 to 15.88 KB. [ABSTRACT FROM AUTHOR]

This paper designed a protection scheme for utility grid with high share of renewable energy (RE) generated from wind energy and solar energy plants. This is based on extraction of features from the current using Stockwell transform (ST), Hilbert transform (HT), and alienation coefficient. A Stockwell index (SI) is designed by extracting current features using ST, a Hilbert index (HI) is designed by extracting current features using HT, and an alienation index (AI) is designed by extracting current features using an alienation coefficient. A fault index (FI) is formulated by multiplying the SI, HI, AI, and WF (weight factor). This FI is implemented for fault detection. Fault classification is achieved considering number of fault phases and ground fault index (GFI). This GFI is designed by processing zero sequence currents applying ST. GFI effectively identifies the ground involved during event of a fault. A designed protection scheme is effective to identify faults in the scenario of high RE share and during various cases of study which includes the variations of fault impedance, different fault occurrence angles (FOA), fault incident at different nodes, and noisy condition. This protection scheme effectively discriminates the fault events from the operational events such as feeder operation, load, and capacitor switching. Performance of hybrid protection method formulated in this paper is better relative to alienation coefficient-based protection scheme (ACPS) reported in literature. The ACPS has maximum error and mean error of fault detection equal to 9.54% and 5.99%, respectively, which is relatively high compared to the respective values for the proposed method which are 1.89% and 0.978%, respectively. ACPS is effective for detecting the fault events in noise level of 30 dB SNR (signal-to-noise ratio) whereas the proposed method effectively identifies the faults in the high noise scenario of 20 dB SNR. [ABSTRACT FROM AUTHOR]

Summary: The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak‐to‐average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions. [ABSTRACT FROM AUTHOR]

Summary: This paper combines additive homomorphic encryption and consortium blockchain technology to provide privacy and trust. Additionally, a dynamic energy pricing model is formulated based on the demand response ratio (DRR) of the load demand of prosumers to address fixed energy pricing problems. The proposed dynamic pricing model includes demurrage fees, which is a monetary penalty imposed on a prosumer, if it failed to deliver energy within the agreed duration. Furthermore, this paper also designs and analyzes a threat model of the proposed system. Experimental evaluations show the effectiveness of the proposed model with regards to low transaction cost, the minimum execution time for block creation, the privacy of prosumers and dispute resolution of demurrage fees. Moreover, the proposed scheme reduces the average system overhead cost up to 66.67% as compared to 33.43% for an existing scheme. Additionally, the proposed blockchain proof‐of‐authority consensus average hash power is minimized up to 82.75% as compared to 60.34% for proof‐of‐stake and 56.89% for proof‐of‐work consensus mechanisms. [ABSTRACT FROM AUTHOR]

Summary: The development of cost‐effective carbon materials with tunable porosity for energy storage applications is challenging. In this study, a facile one‐step in situ chemical activation process was adopted to synthesize porous graphitic carbon sheets (GCSs) from a tissue paper. The resulting GCS exhibits high specific surface area, large pore volume, and a highly graphitized carbon structure with graphene‐like morphology. Sulfur‐infused GCSs with optimum sulfur loading along with GCS‐coated separator deliver a high initial discharge capacity of 1643 mAh g−1 at 0.1C rate. Moreover, when the electrode is tested at a high current rate of 1C, the cell delivered excellent cyclic stability of 652 mAh g−1 over 200 cycles. The improved electrochemical performance attributes to the unique properties of GCS, which functions as an efficient host matrix as well as an interlayer by constraining the dissolved polysulfides in addition to the enhancement in ionic and electronic conductivities. [ABSTRACT FROM AUTHOR]

This paper reviews the recent hybrid photovoltaic thermal (PV/T) structural/geometrical topologies to highlight the state of the art on this form of collector approaches and designs for both liquid and air-based systems. The review focuses on the development of the typical flat-plate collector - as an essential part in the PV/T system - in terms of new concept and novel configurations and specifically on the design of these collectors that use air or liquid as a heat transfer medium and their ability to extract useful heat from the back surface of the PV panel. Different mechanisms of fluid flow either natural or forced are considered. Many different design configurations for hybrid PV/T collectors have been cataloged and evaluated. It is shown that at least 30 distinct configurations have been introduced in the literature in the last 5 years. The paper concludes with identifying the major factors that affect the performance of typical PV/T systems and lead to effective enhancement of the heat removal mechanisms thus improving the electrical and thermal solar conversion efficiencies. This paper should serve as a significant form of reference for any future development in the design of the PV/T concept. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Sustainable development of renewable energy sources is one of the most important themes that humanity faces in this century. Wide use of renewable energy sources will require a drastically increased ability to store electrical energy. Electrochemical energy storage devices are expected to play a key role. With the increased demand in flexible energy resource for wearable electronic devices, great efforts have been devoted to developing high-quality flexible electrodes for advanced energy storage and conversion systems. Because of its high specific surface area, good chemical stability, high mechanical flexibility, and outstanding electrical properties, graphene, a special allotrope of carbon with two-dimensional mono-layered network of sp2 hybridized carbon, have been showing great potential in next-generation energy conversion and storage devices. This review presents the latest advances on the flexible graphene-based materials for the most vigorous electrochemical energy storage devices, that is, supercapacitors and lithium-ion batteries. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The organic Rankine cycle (ORC), as the most promising technology for engine waste heat recovery, has many configurations available to match the complicated engine waste heat conditions. This paper is aimed at revealing the characteristics of the engine waste heat condition for which different ORC configurations are matched so as to give selection guidance. The engine waste heat sources are characterized by the exhaust gas temperature (300°C-650°C) and the heat ratio of the high temperature waste heat to low temperature waste heat (0.5-1.5). Four mostly investigated configurations including the single-loop ORC, the cascade dual-loop ORC, the dual-pressure ORC, and the CO2 transcritical Rankine cycle (CTRC) are selected and modified to enable efficient utilization of all waste heat sources. Their thermodynamic performance is optimized for different waste heat characteristics. The results show improvements in net power for all configurations when exhaust gas temperature and heat ratio rise from 300°C, 0.5, to 650°C, 1.5, respectively, of which the high-temperature split CTRC (HTS-CTRC) recorded the largest increase of 183.58%, while the split regenerative ORC (SR-ORC), dual-pressure ORC (D-ORC), and split cascade ORC (SC-ORC) increased by 17.41%, 23.71%, and 35.45%, respectively. Two selection maps are established to provide general references for quickly selecting the configuration according to different waste heat characteristics whatever the engine type and operating conditions. [ABSTRACT FROM AUTHOR]

The space-time kinetics of ADS Subcritical Reactor (ADSR) under beam transients play an important role in ensuring the reliability of the control and safety system of the accelerator-driven subcritical system (ADS). In order to elucidate the effect of high heterogeneity of neutron flux density in space-time on the neutron space-time kinetics of ADSR under different subcriticality and beam transients, in this paper, the multimode core few-group constants, which is more adaptable to the beam transients, is used to investigate the mechanism of neutron flux density, core few-group constants, and kinetic parameters of ADSR under the synergistic effect of external neutron source, subcriticality, and the beam transients. The results show that with the deepening of the subcriticality, with the increasing role of external neutron source, the neutron flux density of the ADSR has a higher heterogeneity of space-time, which leads to a larger difference in the core few-group constant library of different modes, and thus a larger difference in the kinetic parameters (especially the neutron generation time) of different modes. Therefore, in the study of the influence mechanism of neutron space-time kinetics of ADSR under beam transients, the multimode core few-group constants should be used, and the influence of external neutron source and subcriticality on the kinetic parameters, especially the neutron generation time, should be emphasized. [ABSTRACT FROM AUTHOR]

The development of modern computational fluid dynamics (CFD) technology provides many preliminary references for experimental design. In addition, the CFD calculation results verified by experiments can display enormous microdata in areas that are difficult to measure through experiments, as an extension. This can make measurements more reasonable and effective, shorten measurement times, and save manpower and capital significantly. Therefore, it is vital to verify the accuracy of CFD calculation results, especially in cases of complex structures and multiphase flows. The results of model tests can be utilized in a prototype experiment by properly designing a test section and selecting a working fluid if the Reynolds similarity criterion is met. Optical measurement technology is a noninvasive measurement method, and the impact on the flow field can be almost negligible. It is advised to use transparent materials and prepare a refractive index-matching (RIM) fluid to obtain a good optical path. Polymethyl methacrylate (PMMA) is widely used in flow field visualization experiments because of its good light transmission and mechanical strength. This review is aimed at introducing the current status of different flow field measurement techniques; moreover, it is intended to help the readers to become more familiar with the principles of RIM, the characteristics, applications, and usage suggestions of various RIM fluid schemes of PMMA, providing references for researchers in the design, preparation, and conducting stages of flow field visualization measurement experiments. This review is divided into five sections. In the introduction section, Chapter 1, relevant research developments and related results of flow field measurements are presented, followed by the innovations and benefits of this paper. In Chapter 2, the flow field visualization measurements are presented and a derivation is shown. In Chapter 3, some RIM fluid schemes of PMMA and their applications are given, which are very valuable for peers. In Chapter 4, the measurement and analysis of some physical properties are described. In the RIM process, it is necessary to focus on the RI, density, dynamic viscosity, compatibility, stability, safety, and cost of RIM fluids. These factors greatly impact the accuracy of experimental results, experimental progress, and safety of the experimenters. Based on the analysis and our practical experience, some suggestions are given for preparing and using RIM fluids. In the conclusion section, Chapter 5, the results and practical implications of this paper are summarized. [ABSTRACT FROM AUTHOR]

In recent years, the Chinese Central Government has put great emphasis on the marine economy, since it has been a new driving force of the national economic development. Yet, the relationship between marine economic development and energy efficiency remains unknown. In this paper, we investigate whether marine economic development affected energy efficiency. We focused on the Zhoushan Archipelago, the first National New Area for marine economic development in China. We applied panel data approach to construct the counterfactual of Zhoushan Archipelago. We compared Zhoushan and its counterfactual, the synthetic Zhoushan, and viewed the difference between them, after the Zhoushan Archipelago New Area, as the impact of marine economic development on energy efficiency. We found that compared with its counterfactual, the real Zhoushan had a more substantial improvement in energy efficiency after the National New Area construction. We estimated that the construction of the National New Area for Marine Economy increased energy efficiency by about 10 percentage points. We also applied the bootstrapping technique to illustrate the significance of the estimated results. The results suggest that the Zhoushan Archipelago New Area construction had a statistically significant impact on energy efficiency. In addition, we conducted two tests, including leave-one-out tests and permutation tests, to check the robustness of estimated results, which also serve as an illustration of significance of the estimated results from an alternative empirical perspective. The results from these two tests are similar to each other, both indicating that the Zhoushan Archipelago New Area construction had a statistically significant positive impact on energy efficiency. Overall, our results suggest that marine economic development had a sizable improvement in energy efficiency. [ABSTRACT FROM AUTHOR]

As a result of market liberalisation, a large number of electricity retailers have emerged in the electricity market. Acting as the intermediaries between the electricity producers and the customers, the electricity retailers aim to balance the supply and demand and shoulder substantial risks generated from both sides. Due to the randomness of the electricity load, it is difficult for electricity retailers to make an accurate electricity purchasing plan in advance to meet customer demand. This deviation leads to a proportion of spot electricity purchases that require a higher purchase cost. As a result, one of the most serious concerns facing electricity retailers is how to improve their balancing abilities and reduce power purchase deviation. In contrast to previous research, which has generally recommended that electricity retailers invest in energy storage systems or develop optimised purchasing strategies, this paper proposes a new strategy for the electricity retailers, which is renting external flexible resources to solve the market uncertainty of the electricity retailers, thereby lowering purchase costs and increasing profits. The proposed business model makes use of the cloud energy storage to solve the supply-demand imbalance issue of electricity retailers. The cost calculation model and decision optimisation model have been established in the process of renting cloud energy storage. Charging and discharging cloud energy storage have been separately rented to deal with different positive and negative load deviations, which can simplify the optimisation model. As an experimental paper, the proposed model has been tested in the PJM power market in the United States and the New South Wales power market in Australia. The findings confirm that renting the cloud energy storage capacities can significantly reduce costs and maximise profits for the electricity retailers when compared to the situation without the cloud energy storage. The biggest saving can reach 24.5% in the PJM market. With the rapid fall of battery prices, the advantage of the proposed strategy will be more obvious. [ABSTRACT FROM AUTHOR]

Summary: In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco‐friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs‐based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs‐based power systems and future research dimensions in this area. [ABSTRACT FROM AUTHOR]

Summary: Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro‐electronic interfaces, and energy management strategies (EMS). Significant issues of battery‐powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated‐based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC‐DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces. [ABSTRACT FROM AUTHOR]

Summary: Energy storage systems have been in the spotlight for the past decade as they offer tangible solutions to the ever‐growing pollution problem faced by the planet. These storage systems, primarily lithium‐ion based, power most of the mobile devices and electric vehicles (EVs). Substantial efforts are being made to electrify every mode of transportation to combat climate change. Accurate state‐of‐charge (SOC) and state‐of‐health (SOH) assessment of lithium‐ion batteries play an important role for determining the available range in EVs. Research in this area of capacity estimation demands extensive curated battery parameter data such as voltage, current, and temperature. Performing repeated experiments to collect these data is expensive and tedious. Also, lack of diversity in open access datasets has limited research in this area. This paper introduces a generative adversarial network (GAN)‐based approach for data augmentation. This technique enables expansion of sparse datasets in‐turn enhancing the learning capability of Neural Networks used for SOC/SOH estimation. A time series GAN is used in the present work to produce synthetic data. This technique was evaluated on two publicly available battery parameter datasets to test its effectiveness. A Kullback‐Leibler Divergence value of 0.2317 and 1.0572 was obtained for the battery dataset obtained from NASA prognostics repository and Oxford battery degradation dataset, respectively. The contributions of the paper include: (a) synthetic time‐series data augmentation of battery parameters, (b) high‐fidelity diverse data generation of battery profile data such as voltage, current, temperature, and SOC. [ABSTRACT FROM AUTHOR]

Summary: Many electricity consumers around the world are billed electric peak load charges. In the United States, these demand charges can contribute up to 70% of a consumer's electricity bill. Research has shown that the financial impact of these charges to the consumers can be reduced by acting on the intelligence provided by peak electric load day (PELD) forecasts. Unfortunately, published research detailing PELDs forecasting methodologies for facilities adopting behind the meter renewable electricity generation (BTMREG) is non‐existent. This paper contributes: (a) a PELD forecasting methodology applicable to consumers with BTMREG. A case study demonstrates the methodology's ability to achieve 93% of the potential savings in kW, translating to demand charge reductions of approximately US$ 142 129 per year. (b) The first side‐by‐side performance comparisons of electric load and PELD forecasting models for scenarios with and without BTMREG. (c) The first PELD forecasting model savings comparison for scenarios with and without BTMREG. Experimental results suggest that counterintuitively, BTMREG adoption can translate into higher peak load savings for electricity consumers. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a unique simulation framework for testing and validating multiple operation issues of grid connected microgrids. The framework is widely adjustable and enables various control actions, for example, load shedding, demand response, power curtailment, and integration of battery energy storage systems (BESS). The microgrid operation framework runs several algorithms, providing data for the optimization of the actors at different time scales. In the first part of the paper, the framework, three predictor algorithms, and the network topology used for the tests are presented in detail. Balancing power requirements of microgrids are dominantly determined by uncertainties of intermittent renewable energy‐based generation and changes in consumption. Beyond the environment development, the aim of the present research is to quantify the temporal change of balancing needs between certain confidence intervals. Accordingly, in the second part of the paper, possible approaches to determine the balancing power needs are presented, including the stochastic approach of the new System Operation Guideline (SO GL). The last part of the paper presents error characteristics of the predictor algorithms and balancing power needs are calculated in the context of the European balancing market terminology. Daily and seasonal temporality of errors is examined. The authors recognized that in transition to a more flexible distribution grid operation, the importance of possible microgrid market value propositions is increasingly appreciated. Future research perspectives include physical demonstration and validation of possible ancillary service strategy planning for microgrids. [ABSTRACT FROM AUTHOR]

The academic and policy-oriented literature increasingly quantifies the wider effects of energy policy on the macroeconomy. However, the spillovers from economic policies to energy use are less frequently recognised, meaning that many policymakers who strive to improve productivity whilst simultaneously targeting emission reduction fail to consider these interactions. This paper addresses this issue by using simulation results generated by introducing an exogenous increase in labour productivity in a computable general equilibrium model calibrated with data from the United Kingdom. Theoretical analysis suggests that increasing labour productivity can have positive or negative effects on employment levels and energy use. However, the simulation results show that in the context of a developed open economy, improved labour efficiency will increase employment, a key policy objective, but simultaneously increase energy use. A key policy implication is that this work highlights the need for policy frameworks that explicitly acknowledge and quantify the interconnections between national economic policy strategies and energy policy objectives. Secondly, it shows that in practice, policies to reduce carbon emissions need to be strengthened alongside policies to improve productivity successfully implemented. [ABSTRACT FROM AUTHOR]

Load forecasting is an integral part of the energy study unit to schedule the generating unit by the load demand. Many studies were conducted on load forecasting based on real power demand; however, very few papers were published on reactive power demand. In this research work, an attempt has been made to predict the requirement of reactive power as a function of demand for real power. Household loads are considered for evaluating the demand for reactive power as a critical load. The attempt has been made based on the data collected from the laboratory experimental setup for one year. The load forecasting requires time series analysis of the data set along with error minimization between predicted values and actual value; therefore, the global flower pollination algorithm along with Holt-Winters' exponential model has been used to predict the reactive power. Autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models have been used as benchmarking models to evaluate the effectiveness of the model under various conditions. Python-developed model has been used to predict the demand for reactive power, and a MATLAB model has been developed to optimize the cost function. A detailed comparative analysis of the proposed model along with some well-established optimized models such as GA, PSO, and FPA has been presented related to evening peak demand for a microgrid architecture in Conclusion. The analysis includes median values of different quantities such as nMBE, nMAE, nRMSE, and RMSE. Normalized MBE, indicating underestimation and overestimation, is negative for ARIMA but 0.42 for HW-GFPA during validation and 0.43 for the testing data set. Normalized RMSE, measuring the variance between actual and forecasted values, is lowest at 0.803 for proposed HW-GFPA during validation and 0.799 for testing. [ABSTRACT FROM AUTHOR]

Anisotropic and heterogeneous solids, comprising a minimum of two or more elements with different properties, appear pervasively in rock materials including the pore structure and mineral composition of quartz and granite and usually have extensive applications in construction aggregates, dimension stone, geotechnical engineering, and petrology/geology. The elastic stress fields of inhomogeneous materials or composites inevitably change due to the presence of heterogeneous microstructure under applied external conditions, and hence the total mechanical and physical properties are affected by the temperature and pore pressure fluctuations beneath the surface. In this paper, the thermoporoelastic (TPE) approach on the basis of eigenstrain concept is introduced to predict the stress fields in fluid-saturated porous geologic materials like hydrocarbon reservoirs or aquifers by accounting for the coupling between thermal, poroelastic, and mechanical effects. It is an extension of the micromechanical theory that also incorporates thermal effects like pore pressure changes and temperature alternations. In addition, the TPE approach provides an important multiphysical modeling tool for understanding subsurface fluid-rock interactions and stress states in applications like unconventional oil/gas and geothermal energy. [ABSTRACT FROM AUTHOR]

This paper presents a performance comparison between switching and reduced-order models of grid-following inverters. Different models have been developed to study the inverter's operation using MATLAB and PSIM simulation tools. These models offer a tradeoff between accuracy and computational burden. The switching model fully represents the discontinuities in the inverter due to switching, but such a model demands high computational resources. The inverter representation is simplified using reduced-order models, which is common in the literature. However, the fidelity of these simplified models might not be adequate for some applications. Therefore, it is essential to investigate the performance of such models closely to ensure their suitability depending on the purpose of the analysis. Hence, a comparative study between switching and reduced-order models is presented here by simulating different scenarios and operation conditions. The results have validated the efficacy of the reduced-order model in representing the grid-following inverter's operation in both steady state and response to a step change resulting from active or reactive power variation. Unlike the switching model, the findings illustrate that the reduced-order modeling fails to represent harmonics and losses. After simulating different scenarios, this work highlights the main differences between the switching and reduced-order models to facilitate the choice of the proper modeling method for researchers. [ABSTRACT FROM AUTHOR]

This paper proposes an electric propulsion system model for computationally efficient multiobjective optimization considering overall driving cycles using coupled electromagnetic–thermal 1D simulation. The proposed system model uses equivalent circuit networks rather than finite element method to obtain high computational efficiency because considering both driving cycles and motor temperature change during optimization requires considerable computational cost. The magnetic saturation effect and temperature change of traction motor during operation are examined by constructing look-up tables. This study first integrated the lumped parameter thermal network into the system model to consider the motor temperature change without any iterative process. The 1D simulation results over urban and highway driving cycles indicate that the changes in the induced current, efficiency, and motor temperature could be predicted while driving. The Pareto optimal solution of traction motor cross-sectional geometry including reduction ratio is successfully obtained by multiobjective optimization with the maximum output torque constraint. Constructing the proposed system model once, optimal design for multiple target driving cycles can be obtained without any driving cycle analysis. In addition, high computational efficiency indicates that the proposed system model is practical in the early design stage of various electric propulsions. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective and simultaneous two-problem allocation of a hybrid distributed generation (HDG) system comprises of solar panels, wind turbines, and battery storage is proposed in a 33-bus unbalanced distribution network which can decrease total losses and improve power quality (PQ). The PQ indices are defined as voltage swell, total harmonic distortion, voltage sag, and voltage unbalance. In this study, the two problems of hybrid system design and its allocation in the distribution network are solved simultaneously. In the allocation problem, the HDG is placed ideally in the network to reduce energy losses and enhance PQ indices. The HDG is measured to minimize the cost of energy generation, including the initial investment, maintenance, and operation costs. The decision variable including the size of HDG components and its location is optimally determined via escaping bird search (EBS) algorithm which is inspired by the maneuvers of the swift bird to avoid predation. The results cleared that the proposed methodology using the wind and solar resources integrated with battery storage reduced the losses, voltage swell, total harmonic distortion, voltage sag, and voltage unbalance by 34.31%, 49.60%, 0.25%, 40.19%, and 2.18%, respectively, than the base network via the EBS and the results demonstrated the better network performance using all renewable resources against wind or solar application only. The outcomes demonstrated the superiority of the EBS in achieving the highest improvement of the different objectives compared with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO). Moreover, the superior capability of the EBS-based methodology is proved in comparison with previous studies. [ABSTRACT FROM AUTHOR]

He-Xe gas-cooled micro-molten salt reactor (HX-micro-MSR) is a novel reactor concept that integrates the technologies of molten salt reactor and closed Brayton cycle (CBC), which has a more compact structure compared with the other types of micro-MSRs. The reactor system probed in this paper has a thermal power of 2.5 MWth and consists of the core, fission gas collecting container, and CBC system. The He-Xe gas flows through the coolant channels in the core to transfer the nuclear heat to the CBC for electricity production. To withstand the large pressure difference between the He-Xe gas coolant (7 MPa) and molten salt (nearly atmospheric pressure), the sizes and wall thickness of coolant channels are analyzed and optimized to be 8 mm and 0.5 mm, respectively. In total, 1050 coolant channels are regularly arranged in the core to effectively remove the nuclear heat. The enrichment of U-235 in the fuel UF4 is determined to be 61% to meet the burnup goal of 5 years. To minimize the neutron leakage, the thickness of reflector is selected to be 23 cm. Six control drums with the diameter in 10.5 cm and neutron absorber layer thickness in 1 cm are symmetrically placed in the reflector to realize reactivity control under both normal and accident conditions. Radiation shield is designed and analyzed by varying the thickness of multiple shielding layers, and it is found that a combination of W layer in 14 cm and Mg(BH4)2 layer in 56 cm can satisfy the criterion of radiation dose (<1 mSv/h). The total weight of HX-micro-MSR is estimated to be 35 tonnes and falls into the acceptable range. [ABSTRACT FROM AUTHOR]

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show. [ABSTRACT FROM AUTHOR]

Summary: The cost of platinum group metal (PGM) catalysts is one of the major obstacles in commercializing proton exchange membrane water electrolyzers (PEMWEs). The non‐PGM substituents are often more financially beneficial but low in activity and durability in the acidic environment. In this study, cobalt phosphide catalysts, which are promising non‐PGM alternatives for the hydrogen evolution reaction (HER) and have enhanced durability and single‐cell performance, were fabricated directly on carbon paper using the pulse electrodeposition method. As the dissolution potential (reported as ‐x vs saturated calomel electrode) of the pulse electrodeposition shifted in the positive direction, the P/Co ratio of the Co‐P‐x catalysts increased because of severe Co dissolution. Among the catalysts, Co‐P‐0.6, Co‐P‐0.5, and Co‐P‐0.4 (where the number indicates the negative dissolution potential) were rapidly degraded in acid, whereas Co‐P‐0.3, Co‐P‐0.2, and Co‐P‐0.1 showed high stability because of the relative amounts of CoP and Co2P phases. The acid‐dissolved Co‐P‐0.3 catalyst showed the best half‐cell performance (an overpotential of 143.85 mV at 10 mA/cm2) and durability, and the P‐Co and Coδ+ surface states are critical for its performance. Single‐cell tests using the Co‐P‐0.3 cathode revealed its remarkable performance of 1.89 A/cm2 at 2.0 Vcell, indicating its promise as a non‐PGM cathode material for PEMWEs. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design and implementation of a control for an electric vehicle charger to provide vehicle to grid (V2G) services while reducing the current total harmonic distortion (THD). The control is composed of two current loops. The main one controls the active and reactive powers in a decoupled way, and the second one, focuses on the reduction of the fifth order harmonic component. After explaining the mathematical modelling, the methodology and the experimental setup, the results of the experimental validation are shown. Several experiments were conducted on a scale‐based charger connected to a 400 V grid to observe the performance of the proposed control. Results have shown that the developed control enhances the power quality by reducing the fifth harmonic component by 76.34%, and hence, the current THD from 6.06% to 2.82% and that can successfully provide voltage and frequency regulation without adding extra weight or volume. The proposed strategy can also be extrapolated to other harmonic orders; therefore, it is suitable for grids with high components of certain harmonics. Novelty Statement: The paper presents the design and implementation of a control strategy for vehicle chargers aimed at reducing the current THD produced by a specific voltage harmonic of the grid. The control strategy is simple to implement, robust, and does not require additional elements, and thus, not extra weight or volume is added. Thanks to the double‐loop, it allows independent control of the active and reactive power and the minimization of the nth harmonic from the current. For the experimental test, the proposed control strategy has been used to develop a Fifth Harmonic Controller (FHC) to suppress the fifth harmonic from the current. [ABSTRACT FROM AUTHOR]

Summary: Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system. [ABSTRACT FROM AUTHOR]

Summary Flexibilities offered by gas‐fired power plants have strongly increased their usage in power systems. Increasing the penetration rate of gas‐fired power plants has made it necessary to coordinate gas network expansion planning with electricity network expansion planning. In this paper, a decentralized model for expansion of gas and electricity networks is introduced that coordinates investment in these networks while taking into account their privacy. Alternating direction method of multipliers is used to decompose the centralized gas and electricity expansion problem into two separate expansion problems. The proposed method aims to minimize the investment and operation cost of interconnected gas and electricity networks during the planning period. The resultant problems are mixed‐integer nonlinear programs, which are then linearized and transformed into mixed‐integer linear programming models to facilitate the optimality conditions. Distributed optimization provides a cyber‐physical system in which gas and electricity networks are managed separately while a coordination link is provided through the cloud computing layer. However, coordinated operation through a cloud layer keeps the control‐loop subject to cyber‐attacks. In this paper, the standard form of the proposed decentralized model is modified to secure it against cyber‐attacks. Finally, the effectiveness and applicability of the proposed method are tested and verified over a real case study that has a high penetration level of gas consuming generation units. The proposed cyber secure model not only converges faster than a standard model (ie, computationally more efficient than conventional methods) but also keeps optimality of found solution in a secure fashion. The satisfactory performance of the proposed model is also demonstrated against centralized, leader‐follower, and independent expansion planning models. [ABSTRACT FROM AUTHOR]

Summary: The collection of high quality battery datasets is critical for applications such as State‐of‐Charge or State‐of‐Health estimations. Missing features within the dataset is a challenging problem. The issue is further exacerbated in on‐field electric vehicle applications. One method to resolve this challenge is to impute missing battery parameter values with best possible estimates. In this paper, we explore a self‐attention technique for imputing missing battery parameters. A masked self‐attention block enables accurate estimates of missing battery data. The introduced technique helps provide potential solutions to resolve the issue of missingness in battery parameters. We provide two primary contributions, (a) we illustrate the effectiveness of self‐attention mechanism to provide best estimates of missing battery parameters, (b) we highlight the efficacy of the approach over available state‐of‐the‐art imputation models. We release the code used in this scenario in the results section of our paper. This will allow researchers to effectively reproduce the results presented here.Self‐attention based Imputation Technique [ABSTRACT FROM AUTHOR]

Summary: Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

Summary: Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium‐ion battery (LIB) and a supercapacitor (SC)‐based HESS (LIB‐SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal‐sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system‐level optimizations, include component sizing (CS) and power‐energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB‐SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB‐SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB‐SC HESS are provided to inspire researchers for further studies. Highlights: Lithium‐ion battery (LIB) and supercapacitor (SC)‐based hybrid energy storage system (LIB‐SC HESS) suitable for EV applications is analyzed comprehensively.LIB‐SC HESS configurations and suitable power electronics converter topologies with their comparison are provided.System‐level optimization of LIB‐SC HESS and generalized steps involved in implementing the optimization algorithm for component sizing (CS) and power‐energy management strategy (PEMS) are discussed.A rigorous study on CS and PEMS is presented to develop efficient LIB‐SC HESS.Current challenges and future recommendations for the development of LIB‐SC HESS for EV applications are provided. [ABSTRACT FROM AUTHOR]

Summary: Integrated energy system (IES) can improve energy efficiency, reasonable configuration of low‐carbon equipment in IES can effectively reduce carbon emissions and achieve carbon peaking and carbon neutrality. Under the background of carbon peaking and carbon neutrality, this paper proposes a configuration method of low‐carbon equipment in IES considering life cycle carbon emissions and ladder‐type carbon trading cost. Firstly, the IES architecture of animation and cartoon industry parks in Sino‐Singapore Tianjin eco‐city is analyzed, and the models of "electrical/heating/cooling" energy supply equipment and integrated energy storage equipment are built. Secondly, the two‐stage operation of power to gas (P2G) is refined, and P2G is subdivided into equipment combination of electrolyzer, methanation reactor, hydrogen fuel cell to study the various benefits of hydrogen energy. Then, the life cycle carbon emission calculation method of renewable energy power generation and P2G equipment is proposed, the life cycle carbon emission model of IES is established, and the ladder‐type carbon trading mechanism is introduced to restrict IES carbon emission. On this basis, aiming at minimizing carbon emission cost, energy purchased cost, equipment investment, operation and maintenance cost, and renewable energy power generation curtailment cost, a joint optimal configuration model of renewable energy power generation and P2G is established in order to realize the low‐carbon economic operation of IES. Finally, through multi‐scenario comparative analysis, the correctness and effectiveness of the optimal configuration method of low‐carbon equipment in IES are verified, and the influence of carbon trading mechanism and refining two‐stage operation of P2G on the optimal configuration results are discussed. [ABSTRACT FROM AUTHOR]

Summary: Water‐based hydrogen production is currently an attractive research field, as it provides a greener method to produce hydrogen than existing alternatives. Green hydrogen is expected to progressively replace fossil fuels, which are highly harmful environmentally. This paper presents a critical analysis over time of the main water splitting technologies currently in use for sustainable hydrogen production. As a result of the critical analysis, all the studied techniques have been ordered chronologically in the way that it is possible to understand how new materials have driven to new techniques, more efficient and less expensive. This allows having a complete vision of these technologies. A high level of maturity has been reached in electrolysis, while other techniques still have a long way to go, although many improvements and relevant advancements have been made over the years. The paper offers a global and comparative vision of each technology. From this, it is possible to identify the different paths where efforts are needed to make water‐based hydrogen production a mature, stable and efficient technology. [ABSTRACT FROM AUTHOR]

Summary: The battery pack of electric vehicles (EV) is generally composed of multiple cells in series. Due to the inconsistency between the cells in the production process and use stage, the capacity and state‐of‐charge (SOC) of the cells will be different. We propose an online estimation method based on the charging curve similarity principle in this paper. The proposed method uses a series of charging time differences (CTD) during the charge. By analyzing the CTD curve, the capacity and SOC difference can be achieved. The first‐order resistance circuit model is used for the series charging curve simulation. Further experimental verification is conducted using two groups of four cells in series. In simulations and experiments, the error of the proposed capacity estimation method and the initial SOC error are less than 1%. Finally, the robustness of the proposed method is verified using EV cloud data. The results demonstrate that the proposed method has good robustness at the level of EV cloud data. [ABSTRACT FROM AUTHOR]